A significant advantage of the OR-QuAC benchmark is its quantity.
As a dataset curated via crowdsourcing, OR-QuAC includes tens of thousands of crowd-sourced question-answer pairs and manually de-contextualized queries.
This is a very valuable resource to train large models for the designated open retrieval task and also to provide (weak) supervision data to develop models for other related tasks.
For example, CANARD is often used as an additional (weakly supervised) training dataset for improving systems evaluated on TREC CAsT [1]}.
